Method and system for determining analyte activity

ABSTRACT

C-hemical sensors for detecting the activity of a molecule or analyte of interest is provided. The chemical sensors comprise and array or plurality of chemically-sensitive resistors that are capable of interacting with the molecule of interest, wherein the interaction provides a resistance fingerprint. The fingerprint can be associated with a library of similar molecules of interest to determine the molecule&#39;s activity.

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application is a continuation and claims the benefit ofpriority under 35 U.S.C. §120 of U.S. patent application Ser. No.09/291,932, filed on Apr. 13, 1999 which claims the benefit under 35U.S.C. §119(e)(1) to U.S. Provisional Application No. 60/081,781, filedon Apr. 14, 1998, which is incorporated herein by reference.

FIELD OF THE INVENTION

[0002] The present invention relates to a sensor apparatus useful indetecting trace analytes in a sample, and more specifically determiningthe analyte's biological or physical activity.

BACKGROUND OF THE INVENTION

[0003] There exists a need for determining a variety of molecularproperties that are important in determining a biological, a chemical,or a physical property or activity of a molecule and cataloging theseproperties so that they can be used to identify candidate lead moleculesfor a biological, chemical, pharmaceutical, or industrial application ofinterest.

[0004] Currently methods are used to screen potential drug compounds orbiologics from a collection of molecules of interest (e.g., a library).Such methods use assay techniques that detect a specific activity basedon a molecule's binding affinity, enzymatic activity or otherproperties. Alternatively, lead compounds are generated by computationalmethods, wherein the molecules that possess certain desirable propertiesare defined by shape, dipole moments, surface area, solubility, vaporpressure, hydrophobicity, hydrophilicity, antigenicity and otherphysical properties. These chemical-physical properties are then definedand used to computationally narrow lead compounds to a manageable subsetwhich are then analyzed further by additional screening techniquesdesigned to measure a specific activity in vitro or in vivo by usingadditional high throughput screening techniques.

[0005] Generation of lead compounds is important because not only doesit allow for exploration of a wider range of potential pharmaceuticalagents, but it also offers opportunities for construction of follow-uplibraries that focus on the molecular characteristics represented bythese lead molecules. This in turn is performed to provide yet moreleads with the desired pharmaceutical activity eventually with the hopeof finding a candidate suitable for clinical use.

SUMMARY OF THE INVENTION

[0006] The present invention provides a method for identifying aspecific activity, structure or function of a molecule of interest basedon a sensing device. The sensing device includes an array of sensorsresponsive to a molecule's physical, chemical, or biologicalcharacteristics. The differentially responsive sensors can be opticalsensors, resonance mechanic frequency sensors, and/or electrical sensorsto name a few. Other sensors and arrays are known to those of skill inthe art. For example, in one embodiment the sensing device includes achemical sensor comprising first and second conductive elements (e.g.electrical leads) electrically coupled to a chemically sensitiveresistor which provides a selective electrical path between theconductive elements. The resistor comprises a plurality of alternatingnon-conductive regions (comprising a non-conductive material) andconductive regions (comprising a conductive material) in series. Theelectrical path between the first and second conductive elements istransverse to (i.e., passes through) a plurality of alternatingnon-conductive and conductive regions. In use, the resistor provides achange in resistance between the conductive elements when contacted withan analyte or molecule which interacts with the non-conductive region.The non-conductive region can be made of any material designed tointeract or bind to a class, genus, or specie of analyte.

[0007] The disclosure provides a method and device for identifying aspecific activity, structure or function of an analyte or molecule ofinterest. The method uses a sensing device to produce a characteristicexperimental pattern generated by a plurality of differentiallyresponsive sensors. The pattern has information on the desired molecularproperties for a molecule or analyte of interest. A response pattern isproduced for each member of the library. These patterns are then storedand associated with the library. The library contains patterns formolecules having a desired or known property or activity.

[0008] In one embodiment, a method is provided for screening samples fora specific activity or structure by measuring outputs of a plurality ofchemically-sensitive resistors, each resistor comprising a conductivematerial and a non-conductive material; using results of said measuringto obtain a signal profile, relating to a change in resistance in theplurality of resistors; and comparing the signal profile to apreviously-obtained signal profile indicating a standard sample having aspecific activity, wherein the signal profile is indicative of aspecific activity or a specific structure.

[0009] The disclosure additionally provides a screening system thatincludes a sensor array comprising a plurality of differentiallyresponsive sensors, each sensor capable of providing a signalcorresponding to the sensors interaction with a molecule of interest. Ameasuring device detects the signal from each sensor and arranges theminto a signal profile representing a molecule's characteristics (e.g.,activity, structure, or function). A computer then compares the signalprofile to determine the molecule's activity. Preferably, the computerhas a resident algorithm for comparing the signal profile(s).

[0010] For example, in one embodiment, a sample screening system isprovided, the system including a sensor array comprising at least firstand second chemically-sensitive resistors, each chemically-sensitiveresistor comprising a mixture of non-conductive organic polymer andconductive material compositionally different than said non-conductiveorganic polymer, each resistor providing an electrical path through saidmixture of non-conductive organic polymer and said conductive material,a first electrical resistance, when contacted with a first chemicalanalyte at a first concentration and a second different electricalresistance when contacted with a second analyte, wherein the differencebetween the first electrical resistance and the second electricalresistance of the first chemically-sensitive resistor being differentfrom the difference between the first electrical resistance and thesecond electrical resistance of the second chemically-sensitiveresistor; an electrical measuring device electrically connected to thesensor array; and a computer wherein the electrical measuring devicedetects the first and second electrical resistance in each of thechemically-sensitive resistors and the computer assembles the resistanceinto a sensor array signal profile, wherein the computer is operative tocompare the signal profile to a signal profile obtained from a standardsample having a specific activity, wherein the signal profile isindicative of a specific activity or a specific structure.

BRIEF DESCRIPTION OF THE DRAWING

[0011] These and other objects of the present invention will now bedescribed in detail with reference to the accompanying drawing, inwhich:

[0012]FIG. 1A shows an overview of sensor design; 1B, shows an overviewof sensor operation; and 1C, shows an overview of system operation.

[0013]FIG. 2 presents the relative differential resistance responses forvarious conducting polymer composite sensors to three representativealcohols.

[0014]FIG. 3 shows a plot of pI₅₀ predicted by equation 3 versus theactual experimental value. Horizontal error bars represents an averageexperimental error and vertical error bars correspond to the standarderror of equation 3. The line represents perfect agreement betweenexperiment and prediction.

[0015]FIG. 4 shows a diagram illustrating the M and A steric parameters.

[0016] FIGS. 5A-5D show a table where the first three columns give thename of the alcohol, its experimental pI₅₀ value and run in which it wasanalyzed (and the bubbler in which it was placed). The remainder of thetable lists the responses (expressed as percent change in electricalresistance relative to base line resistance) of the 19 differentpolymer/carbon black sensors upon exposure to the alcohols at 5% oftheir respective saturated vapor pressures. The standard deviation ofthe responses over ten trials are given in

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] The approach described herein uses experimental data (e.g. asignal profile, such as a resistance fingerprint) that is generated byan array of differentially responsive sensors. Such sensors include, forexample, chemically-sensitive resistor of a sensing array, such as thatfound in an “electronic nose” as described in U.S. Pat. No. 5,571,401(the disclosure of which is incorporated herein), when it is exposed toa molecule of interest. The change in the electrical resistance of achemically-sensitive resistor in such a sensing array can be related tothe sorption of a molecule of interest to the non-conductive regions ofthe chemically-sensitive resistor. The signals produced by a pluralityof chemically-sensitive resistors having individual sorption criteriathus provide information on a number of chemically important properties,such as the hydrophobicity, molecular size, polarity, andhydrogen-bonding interactions of a molecule of interest, thus, forexample, creating a resistance profile or fingerprint of the molecule ofinterest based upon its chemical properties.

[0018] Another type of sensor, includes, for example, hydrogelscontaining a crystalline colloidal array (CCA) as disclosed in U.S. Pat.No. 5,854,078 (the disclosure of which is incorporated herein byreference). Such hydrogels undergo a volume change in response to aspecific chemical species. As the hydrogels are modulated in size thelattice spacing of the CCA embedded therein changes as well. The lightdiffraction, therefore, indicates the presence or absence of the stimulithat causes the volume of the hydrogel to change.

[0019] Yet another type of sensor includes those disclosed in U.S. Pat.No. 5,512,490 to Walt et al. (the disclosure of which is incorporatedherein by reference). The optic sensor of this system is comprised of asupporting member and an array formed of heterogeneous, semi-selectivethin films which function as sensing receptor units and are able todetect a variety of different analytes and ligands using spectralrecognition patterns. Each formulation of sensing receptor unitcomprising the array of the optical sensor reacts with a plurality ofdifferent chemical compounds and compositions; and for each individualchemical compound, provides a spectral response pattern over time (bychanges in energy intensity, or by changes in wavelength or both ofthese parameters) which is indicative of the event and consequence ofthe reaction with a single compound. The array also generates spectralresponses and patterns from mixtures of different compounds based uponthe optical responses from each of the individual compounds forming thismixture.

[0020] By “molecule of interest” or “analyte” is meant any number ofvarious molecules. For example a molecule or analyte of interest may bea nucleic acid (e.g., DNA or RNA), a polypeptide (e.g., an antibody,protein, enzyme), a biochemical (e.g., a lipid, hormone, fatty acids,carbohydrate), pharmaceuticals, a chemical such as organics including,for example, alkanes, alkenes, alkynes, dienes, alicyclic hydrocarbons,arenes, alcohols, ethers, ketones, aldehydes, cyclic hydrocarbons,carbonyls, carbanions, polynuclear aromatics and derivatives of suchorganics, e.g., halide derivatives.

[0021] By “differentially responsive sensors” is meant any number ofsensors that respond to the presence or interaction of a collection ofmolecules with the sensor by providing some measurable change. Eachindividual sensor does not uniquely probe the property of interest, thusany individual signal alone is not sufficient to determine the desiredchemical or biological property of an analyte. Instead the responsepattern of a plurality of sensors is used to obtain the desired activitythrough comparison with a standard response pattern produced by ananalyte with a known activity. Such measurable changes include changesin optical wavelengths, transparency of a sensor, resonance of a sensor,resistance, diffraction of light and/or sound, and other changes easilyidentified to those skilled in the art. Such sensors include, but arenot limited to, crystalline colloidal array (CCA) sensors and variants,such as hydrogels containing CCA, metal oxide sensors, dye-impregnatedpolymers coated onto beads or optical fibers, bulk conducting organicpolymers, and capacitance sensors.

[0022] In a traditional assay method, a desired chemical or biologicalactivity is determined by the response of one designed sensor orinterest. This single response is known to probe the chemical and/orbiological activity of interest, and the magnitude of the sensorresponse is then readily and directly related to the activity ofconcern. For instance, enzymatic inhibition by a certain ligand could bedetermined from an assay that probed directly the amount of substrateconsumed by the enzyme under various conditions or indirectly by theamount of product metabolized under known and calibrated conditions.Different ligands would produce different amounts of substrateconsumption or metabolite products, and the amounts of such woulddirectly indicate the desired biological property, the ligand inhibitionof the enzymatic activity. Another example would be determination of thepresence of a particular nucleic acid sequence in a sample throughinvestigating the response of array of sensors, each of which hadcomplements to different but known or knowable sequences of nucleicacids. The sensor that displayed the highest response change (forinstance, florescence appearance or disappearance, electrochemicalactivity or disappearance of electrochemical activity, etc.) would thenbe uniquely associated with the presence of the sequence of interest inthe sample, through knowledge of the complementary sequence that waspresent on that particular sensing element and association of theknowledge of the complementary sequence with the sequence that must thenbe present in the analyte of interest.

[0023] The present invention utilizes a different approach. A pluralityof differentially responsive sensors, each of which provides measurablesignals in response to a variety of analytes, chemicals, andbiochemicals of concern, is used. The desired chemical or biologicalactivity is not revealed by the response of an individual sensor orindividual sensor response signal, but is instead obtained by patternanalysis of the responses produced by a plurality of differentiallyresponsive sensors in the sensor array device. The sensors may or maynot themselves be selective and predetermined to uniquely probe thechemical or biological property of interest, but the various differingresponse patterns produced by the analytes of interest upon exposure tothe plurality of sensors is correlated with the desired chemical and/orbiological activity after comparison of the response pattern to thepattern produced by an analyte with a known chemical and/or biologicalactivity.

[0024] By allowing interactions between a molecule of interest and adifferentially responsive sensor, the signals can be directly orindirectly related to the properties of the molecule of interest. Forexample, by using a chemically-sensitive resistor in an array of an“electronic nose” it is possible to directly and indirectly relate thesignals of the electronic nose to the properties of the molecules ofinterest. For example, if the significant interactions between amolecule and the binding site of an enzyme are related either directlyor indirectly in the collection of binding constants of that molecule toa non-conductive element of the electronic nose, then it is possible torelate the electronic nose signal to the enzyme's binding properties.

[0025] Once a signature of a set of molecules has been obtained, thesignature profile could be used, with an appropriate training set topredict the activity of any member of the library in a chemicalinteraction of interest.

[0026] As described in the Example below, which is not meant to limitthe present claims, the inventor has demonstrated that an electronicnose is capable of identifying alcohols having chemical characteristicsthat are capable of inhibiting cytochrome P-450 activity. Such chemicalcharacteristics are related to various chemical-physical parameters ofthe alcohol including its three-dimensional structure, side groups,charge, and other parameters known to those of skill in the art.

[0027] The methods and apparatus of the present invention are applicableto a wide range of molecules of interest and types of sensors andarrays. For example, and not by way of limitation, one embodimentprovides a method which can be used to identify polypeptides having abiological function. Such functions include a polypeptide's role as areceptor, receptor antagonist or agonist, enzymatic activity (e.g.,lipases, esterases, proteases, glycosidases, glycosyl transferases,phosphatases, kinases, mono- and dioxygenases, haloperoxidases, ligninperoxidases, diarylpropane peroxidases eposide hydrolases, nitrilehydrotasees, nitrilases, transaminases, amidases and acylases),DNA-binding ability (e.g., histones), antibody activity (e.g., theability to bind an epitope), such antibodies include monoclonal,polyclonal and humanized antibodies to name a few. The activity can bedetermined based on the polypeptides primary, secondary, and/or tertiarystructure as well as its charge, hydrophobicity, hydrophilicity andother polypeptide properties known to those of skill in the art ascompared to a library of similar polypeptide molecules. Thedifferentially responsive sensors (e.g., a chemically-sensitiveresistor) of an array for detection of such properties are designed asdescribed herein or in any number of ways known to those of skill in theart. The differentially responsive sensors do not need to be speciallydesigned to bind a specific polypeptide. For example, where the sensoris of a resistor-type, the elements do not need to be specially designedto bind such polypeptides so long as they are capable of detectingproperties by interactions of the polypeptide or molecule of interestwith the chemically-sensitive resistor.

[0028] In another embodiment, the present invention provides a method ofdetecting the activity of a biological molecule or pharmaceuticalcompound. The method provides contacting a plurality of differentiallyresponsive sensors with a compound or molecule of interest and thencomparing the “fingerprint” (e.g., a resistance fingerprint) or“profile” with the fingerprint of other related molecules having adesired activity or function. Molecules having similar fingerprints areindicative of molecules having similar activities. Such activities canrange from the detection of disease molecules (e.g., viral antigens,bacterial antigens, such as LPS, endotoxin, etc.), carcinogenicmolecules, antibiotic molecules, antiviral molecules, viral compoundsand any number of molecules now known or discovered, so long as they arecapable of interacting with a plurality of sensors thus eliciting achange in for example, optics, resonance, and/or current across thesensor (e.g., increase or decrease in the resistance).

[0029] Learning based and/or pattern-recognition based algorithms areused to identify leads from the library based on the data contained inthe experimental response patterns, without the need necessarily foradditional assays or for additional computations on the remainingmembers of the library. Additionally, one advantage of the invention isthat it provides an experimentally based measure of the molecularproperties involved in the desired binding event. Once a pattern hasbeen recorded for a library, it remains associated with that libraryindefinitely and can be used for other purposes subsequently. Forexample, after screening a library for leads in activity towards a givenbinding site, with a few new examples on another binding site, thepatterns can then be interrogated again to produce leads for this newtarget event without the need to recollect the response patterns nor toreassay the entire library for activity towards that particular newprocess.

[0030] The differentially responsive sensors in the array need not becarefully tailored towards the molecule of interest. Instead, it issufficient that they collectively probe a broad range of molecularproperties, for example, hydrophobicity, polarity, molecular size orshape, chirality, and other chemical-physical characteristics known inthe art. Each individual sensor need not selectively probe theseproperties, nor is it essential that the experimentalist evaluate inadvance which properties are being probed by the array. For example, anarray may have any number of responsive sensors from one to greater than10⁶ In a preferred embodiment, wherein the sensors are resistors thearray would have a significant (>10) number of chemically-sensitivedetectors or resistors, each of which would be at least partiallyresponsive to certain properties that affect molecular binding andrecognition events.

[0031] The signal transduction mechanism through which the analyte ormolecule produces an array response or resistance fingerprint ispotentially quite broad. Many methods are known to those skilled in theart of constructing artificial nose devices. These include arrays ofsurface acoustic wave devices, quartz crystal micro-balances, dye-coatedfiber optics, conducting organic polymers, electrochemical gas sensors,fiber optic micromirrors, composites of insulating organic polymers andconductors, tin oxide sensors, hydrogel based CCA, nucleic acid orprotein based polymers (see for example Ramsey, Graham, Nature Biotech,16:40-44, (1998)), and others readily identifiable to those skilled inthe art. Different types of signal transduction mechanisms could also beused in one array to expand the information contained in the responsepattern produced by the analyte or molecule of interest, for exampleoptical, electrical, and/or resonance.

[0032] When the differentially responsive sensor is a resistor, theresistor comprises a plurality of alternating non-conductive andconductive regions transverse to an electrical path between conductiveleads. Generally, the resistors are fabricated by blending a conductivematerial with a non-conductive material such that the electricallyconductive path between the leads coupled to the resistor is interruptedby gaps of non-conductive material. For example, in a colloidalsuspension or dispersion of particulate conductive material in a matrixof non-conductive material, the matrix regions separating the particlesprovide the gaps. The non-conductive gaps range in path length fromabout 10 to 1,000 angstroms, usually on the order of 100 angstromsproviding individual resistance of about 10 to 1,000 mΩ, usually on theorder of 100 mΩ, across each gap. The path length and resistance of agiven gap is not constant but rather is believed to change as thenonconductive organic polymer of the region absorbs, adsorbs or imbibesan analyte. Accordingly the dynamic aggregate resistance provided bythese gaps in a given resistor is a function of analyte permeation ofthe non-conductive regions. However, it will be recognized thatmaterials which change conformationally, or effect a proton distributionor availability, in response to the binding of an analyte are alsoencompassed by the present disclosure. For example, a non-conductivematerial which results in a proton change upon binding of an analyte cancause an exponential change in the resistance of thechemically-sensitive resistor. In some embodiments, the conductivematerial may also contribute to the dynamic aggregate resistance as afunction of analyte permeation (e.g., when the conductive material is aconductive material such as a polypryole).

[0033] A wide variety of conductive materials and non-conductivematerials can be used. Table 1 provides exemplary conductive materialsfor use in resistor fabrication; mixtures, such as of those listed, mayalso be used. Table 2 provides exemplary non-conductive materials;blends and copolymers, such as the materials listed here, may also beused. Combinations, concentrations, blend stoichiometries, percolation,threshold, etc. are readily determined empirically by fabricating andscreening prototype resistors (chemiresistors) as described below. TABLE1 Major Class Examples Organic conducting polymers (poly(anilines),poly(thiophenes), Conductors poly(pyrroles), poly(aceylenes, etc.)),carbnaceious material (carbon blacks, graphite, coke, C60 etc.), chargetransfer complexes (tetramethylparaphnylene- diamine-chloranile, alkailimetal tetracyanoquino- dimethane complexes, tetrathiofulvalene halidecomplexes, etc.), etc. Inorganic metals and metal alloys (Ag, Au, Cu,Pt, AuCu alloy, Conductors etc.), highly doped semiconductors (Si, GaAs,InP, MoS2, TiO2, etc.), conductive metal oxides (In2O3, SnO2, Na2Pt3O4,etc.), superconductors (Yba2Cu3O7, Ti2Ba2Ca2Cu3O10, etc.), etc. MixedTetracyanoplatinate complexes, Iridium halocarbonyl inorganic/organiccomplexes, stacehed macrocyclic complexes. Etc. Conductor

[0034] TABLE 2 Major Class Examples Main-chain poly(dienes),poly(alkenes), poly(acrylics), carbon polymers poly(methacrylics),poly(vinyl ethers), poly(vinyl thioethers), poly(vinyl alcohols),poly(vinyl ketones), poly(vinyl halides), poly(vinyl nitrites),poly(vinyl esters), poly(styrenes), poly(aryines), etc. Main-chainpoly(oxides), poly(caronates), poly(esters), acyclic poly(anhydrides),poly(urethanes), poly(sulfonate), heteroatom poly(siloxanes),poly(sulfides), poly(thioesters), polymers poly(sulfones),poly(sulfonamindes), poly(amides), poly(ureas), poly(phosphazens),poly(silanes), poly(silazanes), etc. Main-chainpoly(furantetracarboxylic acid diimides), heterocyclicpoly(benzoxazoles), poly(oxadiazoles), polymerspoly(benzothiazinophenothiazines), poly(benzothiazoles),poly(pyrazinoquinoxalines), poly(pyromenitimides), poly(quinoxalines),poly(benzimidazoles), poly(oxidoles), poly(oxoisinodolines),poly(diaxoisoindoines), poly(triazines), poly(pyridzaines),poly(pioeraziness), poly(pyridinees), poly(pioeridiens),poly(triazoles), poly(pyrazoles), poly(pyrrolidines), poly(carboranes),poly(oxabicyclononanes), poly(diabenzofurans), poly(phthalides),poly(acetals), poly(anhydrides), carbohydrates, etc.

[0035] The chemiresistors can be fabricated by many techniques such as,but not limited to, solution casting, suspension casting, and mechanicalmixing. In general, solution case routes are advantageous because theyprovide homogenous structures and ease of processing. With solution caseroutes, resistor elements may be easily fabricated by spin, spray or dipcoating. Since all elements of the resistor must be soluble, however,solution case routes are somewhat limited in their applicability.Suspension casting still provides the possibility of spin spray or dipcoating but more heterogeneous structures than with solution casting areexpected. With mechanical mixing, there are fewer solubilityrestrictions since it involves only the physical mixing of the resistorcomponents but device fabrication is more difficult since spin, sprayand dip coating are no longer possible. A more detailed discussion ofeach of these follows.

[0036] For systems where both the conducting and non-conducting media ortheir reaction precursors are soluble in a common solvent, thechemiresistors can be fabricated by solution casting. The oxidation ofpyrrole by phosphomolybdic acid presented herein represents such asystem. In this reaction, the phosphomolybdic acid and pyrrole aredissolved in tetrahydrofuran (THF) and polymerization occurs uponsolvent evaporation. This allows for THF soluble non-conductive polymersto be dissolved into this reaction mixture thereby allowing the blend tobe formed in a single step upon solvent evaporation. The choice ofnon-conductive material in this route is, of course, limited to thosethat are soluble in the reaction media. For the poly(pyrrole) casedescribed above, preliminary reactions were performed in THF, but thisreaction should be generalizable to other non-aqueous solvent such asacetonitrile or ether. A variety of permutations on this scheme arepossible for other conducting material. Some of these are listed below.Certain conducting materials, such as substitutedpoly(cyclooctatetraenes), are soluble in their undoped, non-conductingstate in solvents such as THF or acetonitrile. Consequently, the blendsbetween the undoped material and plasticizing material can be formedfrom solution casting. After which, the doping procedure (exposure to I₂vapor, for instance) can be performed on the blend to render thesubstituted poly (cyclooctatetraene) conductive. Again, the choice ofnon-conductive materials is limited to those that are soluble in thesolvents that the undoped conducting material is soluble in and to thosestable to the doping reaction. Certain conducting materials can also besynthesized via a soluble precursor material. In these cases, blendsbetween the precursor material and the non-conducting material can firstbe formed followed by chemical reaction to convert the precursormaterial into the desired conducting material. For instancepoly(ρ-phenylene vinylene) can be synthesized through a solublesulfonium precursor. Blends between this sulfonium precursor and thenon-conductive material can be formed by solution casting. After which,the blend can be subjected to thermal treatment under vacuum to convertthe sulfonium precursor to the desired poly(ρ-phenylene vinylene).

[0037] In suspension casting, one or more of the components of theresistor is suspended and the others dissolved in a common solvent.Suspension casting is a rather general technique applicable to a widerange of species, such as carbon blacks or colloidal metals, which canbe suspended in solvents by vigorous mixing or sonication. In oneapplication of suspension casting, the non-conductive material isdissolved in an appropriate solvent (such as THF, acetonitrile, water,etc.). Colloidal silver is then suspended in this solution and theresulting mixture is used to dip coat electrodes.

[0038] Mechanical mixing is suitable for all of theconductive/non-conductive combinations possible. In this technique, thematerials are physically mixed in a ball-mill or other mixing device.For instance, carbon black: non-conductive material composites arereadily made by ball-milling. When the non-conductive material can bemelted or significantly softened without decomposition, mechanicalmixing at elevated temperature can improve the mixing process.Alternatively, composite fabrication can sometimes be improved byseveral sequential heat and mix steps. Once fabricated, the individualelements can be optimized for a particular application by varying theirchemical make up and morphologies. The chemical nature of the resistorsdetermines to which analytes they will respond and their ability todistinguish different analytes. The relative ratio of conductive toinsulating components determines the magnitude of the response since theresistance of the elements becomes more sensitive to sorbed molecules asthe percolation threshold is approached. The film morphology is alsoimportant in determining response characteristics. For instance, thinfilms respond more quickly to analytes than do thick ones. Hence, withan empirical catalogue of information on chemically diverse sensors madewith varying ratios of insulating to conducting components and bydiffering fabrication routes, sensors can be chosen that are appropriatefor the analytes expected in a particular application, theirconcentrations, and the desired response times. Further optimization canthen be performed in an iterative fashion as feedback on the performanceof an array under particular conditions becomes available.

[0039] The resistor may itself form a substrate for attaching the leador the resistor. For example, the structural rigidity of the resistorsmay be enhanced through a variety of techniques:chemical or radiationcross-linking of polymer components (dicumyl peroxide radicalcross-linking, UV-radiation cross-linking of poly(olefins), sulfurcross-linking of rubbers, e-beam cross-linking of Nylon, etc.), theincorporation of polymers or other materials into the resistors toenhance physical properties (for instance, the incorporation of a highmolecular weight, high transition metal (Tm) polymers), theincorporation of the resistor elements into supporting matrices such asclays or polymer networks (forming the resistor blends withinpoly-(methylmethacrylate) networks or within the lamellae ofmontmorillonite, for instance), etc. In another embodiment, the resistoris deposited as a surface layer on a solid matrix which provides meansfor supporting the leads. Typically, the matrix is a chemically inert,non-conductive substrate such as a glass or ceramic.

[0040] Sensor arrays particularly well-suited to scaled up productionare fabricated using integrated circuit (IC) design technologies. Forexample, the chemiresistors can easily be integrated onto the front endof a simple amplifier interfaced to an A/D converter to efficiently feedthe data stream directly into a neural network software or hardwareanalysis section. Micro-fabrication techniques can integrate thechemiresistors directly onto a micro-chip which contains the circuitryfor analog signal conditioning/processing and then data analysis.Ink-jet technology can be used for the production of millions ofincrementally-different sensor elements in a single manufacturing step.Controlled compositional gradients in the chemiresistor elements of asensor array can be induced in a method analogous the way that a colorink-jet printer deposits and mixes multiple colors. However, in thiscase, rather than multiple colors, a plurality of different polymers insolution which can be deposited are used. A sensor array of a milliondistinct elements only requires a 1 cm×1 cm sized chip employinglithography at the 10 μm feature level, which is within the capacity ofconventional commercial processing and deposition methods. Thistechnology permits the production of sensitive, small-sized, stand-alonechemical sensors.

[0041] Preferred sensor arrays have a predetermined inter-sensorvariation in the structure or composition of the non-conductive polymerregions. The variation may be quantitative and/or qualitative. Forexample, the concentration of the non-conductive material in the blendcan be varied across sensors. Alternatively, a variety of differentmaterials may be used in different sensors. In one embodiment, anelectronic nose for detecting an analyte in a sample is fabricated byelectrically coupling the sensor leads of an array of compositionallydifferent sensors to an electrical measuring device. The device measureschanges in resistivity at each sensor of the array, preferablysimultaneously and preferably over time. Frequently, the device includessignal processing means and is used in conjunction with a computer anddata structure for comparing a given response profile to astructure-response profile database for qualitative and quantitativeanalysis. Typically such a nose comprises at least ten, usually at least100, and often at least 1000 different sensors though with massdeposition fabrication techniques described herein or otherwise known inthe art, arrays of on the order of at least 10⁶ sensors are readilyproduced.

[0042] In operation, each resistor provides a first electricalresistance between its conductive leads when the resistor is contactedwith a first sample comprising a chemical analyte at a firstconcentration, and a second electrical resistance between its conductiveleads when the resistor is contacted with a second sample comprising thesame chemical analyte at a second different concentration. The samplesmay be liquid or gaseous in nature. The first and second samples mayreflect samples from two different environments, a change in theconcentration of an analyte in a sample sampled at two time points, asample and a negative control, etc. The sensor array necessarilycomprises sensors which respond differently to a change in an analyteconcentration, i.e. the difference between the first and secondelectrical resistance of one sensor is different from the differencebetween the first and second electrical resistance of another sensor.

[0043] In a preferred embodiment, the temporal response of each sensor(resistance as a function of time) is recorded. The temporal response ofeach sensor may be normalized to a maximum percent increase and percentdecrease in resistance which produces a response pattern associated withthe exposure of the analyte. By iterative profiling of known analytes, astructure-function database correlating analytes and response profilesis generated. Unknown analyte may then be characterized or identifiedusing response pattern comparison and recognition algorithms.Accordingly, analyte detection systems comprising sensor arrays, anelectrical measuring device for detecting resistance across eachchemiresistor, a computer, a data structure of sensor array responseprofiles, and a comparison algorithm are provided. In anotherembodiment, the electrical measuring device is an integrated circuitcomprising neural network-based hardware and a analog-digital converter(ADC) multiplexed to each sensor, or a plurality of ADCs, each connectedto different sensor(s).

[0044] A wide variety of analytes and samples may be analyzed by thedisclosed sensors, arrays and noses so long as the subject analyte iscapable of generating a differential response across a plurality ofsensors of the array. Analyte applications include broad ranges ofchemical classes such as organics such as alkanes, alkenes, alkynes,dienes, alicyclic hydrocarbons, arenes, alcohols, ethers, ketones,aldehydes, carbonyls, carbanions, polynuclear aromatics and derivativesof such organics, e.g. halide derivatives, etc., biomolecules such assugars, isoprenes and isoprenoids, fatty acids and derivatives, etc.Accordingly, commercial applications of the sensors, arrays and nosesinclude environmental toxicology and remediation, biomedicine, materialsquality control, food and agricultural products monitoring, etc.

[0045] The general method for using the disclosed sensors, arrays andelectronic noses, for detecting the characteristics or presence of ananalyte in a sample involves sensing a change in a differentiallyresponsive sensor to the presence of an analyte in a sample. Forexample, where the sensor is a resistor-type sensor, measurement inresistance changes where the chemical sensor comprises first and secondconductive leads electrically coupled to and separated by achemically-sensitive resistor as described above by measuring a firstresistance between the conductive leads when the resistor is contactedwith a first sample comprising an analyte at a first concentration and asecond different resistance when the resistor is contacted with a secondsample comprising the analyte at a second different concentration.

[0046] In one embodiment, a rapid method for individually addressing themembers of the library and individually collecting their responsepatterns is desirable. In this embodiment, gases derived from a fluidare used wherein the analytes are vapors that are to be detected bytheir response on the sensor array. A substrate contains the library(e.g., analyte matrix) of interest whose response patterns are to becollected. The substrate may be cooled in order to reduce the vaporsemanating from the molecules before analysis. The sensors (e.g.“chemically-sensitive resistors”) are also placed within this analysischamber. Either a carrier gas or a vacuum is present in order to insurethat the chamber is not contaminated with residual molecules of a prioranalysis, inlet and outlet ports may be used to manipulate and controlthe gas flow. The sensors themselves may also have a temperaturecontrol, as may the walls of the chamber. Cooling the walls of thechamber will prevent desorption of the impurities during an analysis andheating can be used to clean the chamber of such impurities at asubsequent time. Temperature control of the sensors is beneficial tocontrol the sensitivity, response time, and noise characteristics of thedetectors in the sensor array.

[0047] In this embodiment, the library is cooled so that the vaporpressure of each individual molecular constituent is maintained at abackground level until that particular species is to be analyzed. When aresponse pattern is to be recorded for a molecule of interest, theregion that contains the molecule of interest in the library is heatedto volatilize it. This thereby produces vapors that can be transportedto the detectors of the sensor array. This local heating could beperformed by a laser spot, by an addressable resistive grid of wiresthat contacts the substrate or a portion of the substrate, or by othermethods of heating that are known to those skilled in the art ofgenerating temperature excursions in materials. After the pattern isrecorded for the spot of interest, the temperature is returned to itsset point and another spot is interrogated. An alternative uses multiplesets of detectors in parallel, with accompanying stimuli in parallel, toincrease the throughput of analysis of an entire array if so desired.

[0048] Once a series of patterns has been collected, some initialinformation is desirable in order to identify leads for a particularactivity or function. For example, experimental data might be availableshowing that members 1, 2, and 3 have increasing activity towards theactivity or function of interest. Such activity or function may bederived from known molecules whose activity has been well-characterizedor may be subsequently or contemporaneously measured by additional assaytechniques. Analysis of the patterns produced by the electronic nosearray using neural network or statistical method-based programs wouldthen identify which molecules, or which molecular properties beingprobed by the detectors, showed a correlation with the specific ordesired activity or function. Once this correlation is established,analysis of the remaining patterns would be used to identify moleculeswith similar correlation, and thereby to identify the leads in thelibrary for further interrogation and analysis both experimentally andtheoretically, as appropriate.

[0049] The analysis of a resistance signal pattern (e.g. a resistanceprofile) of the embodiment may be implemented in hardware or software,or a combination of both (e.g., programmable logic arrays or digitalsignal processors). Unless otherwise specified, the algorithms includedas part of the invention are not inherently related to any particularcomputer or other apparatus.

[0050] In particular, various general purpose machines may be used withprograms written in accordance with the teachings herein, or it may bemore convenient to construct more specialized apparatus to perform theoperations. However, preferably, the embodiment is implemented in one ormore computer programs executing on programmable systems each comprisingat least one processor, at least one data storage system (includingvolatile and non-volatile memory and/or storage elements), at least oneinput device, and at least one output device. The program code isexecuted on the processors to perform the functions described herein.

[0051] Each such program may be implemented in any desired computerlanguage (including machine, assembly, high level procedural, or objectoriented programming languages) to communicate with a computer system.In any case, the language may be a compiled or interpreted language.

[0052] Each such computer program is preferably stored on a storagemedia or device (e.g., ROM, CD-ROM, or magnetic or optical media)readable by a general or special purpose programmable computer, forconfiguring and operating the computer when the storage media or deviceis read by the computer to perform the procedures described herein. Thesystem may also be considered to be implemented as a computer-readablestorage medium, configured with a computer program, where the storagemedium so configured causes a computer to operate in a specific andpredefined manner to perform the functions described herein.

[0053] The following Example, is provided to illustrate, but not limit,the scope of the present invention. For example, those skilled in theart will recognize that the methods and systems of the present inventionare applicable to a wide variety of differentially responsive sensors,including optical, sound (resonance), resistance, or other sensors knowto those of skill in the art.

EXAMPLE

[0054] To test the ability of the “electronic nose” to identifymolecules of interest having a particular biological activity selectedfrom a library of molecules of interest, a quantitativestructure-activity relationship (QSAR) was used to predict theinhibitory action of a series of alcohols on cytochrome P-450 anilinep-hydroxylation.

[0055] Polymer synthesis and preparation. Polymers were generallydissolved in tetrahydofuran, except for poly(4-vinylpyridine) andpoly(vinylpyrrolidone), which were dissolved in ethanol, andpoly(ethylene-co-vinyl acetate)(18% vinylacetate), 1,2-poly(butadiene),and poly(butadiene)(36% cis and 55% trans 1-4), which was dissolved intoluene. Each polymer (160 mg) was dissolved in its respective solvent(20 ml) either at room temperature or by heating to 35-40 □ C. forseveral hours. Carbon black (40 mg) was added and the suspensionsonicated for at least 20 minutes.

[0056] Sensor Fabrication. Corning microscope slides were cut into 10mm×25 mm pieces to provide substrate for the sensor. A 7-8 mm gap acrossthe middle of each piece was masked while 300 nm of chromium and then500 nm of gold was evaporated onto the ends of the slides to form theelectrical contacts. Sensors were formed by spin-coating polymer/carbonblack suspensions onto the prepared substrates. The resulting films werethen allowed to dry overnight.

[0057] Measurements. An automated flow system consisting of LabVIEWsoftware, a pentium computer, and electronically controlled solenoidvalves and mass flow controllers were used to produce and deliverselected concentration of solvent vapors to the detectors. To obtain thedesired analyte concentration, a stream of carrier gas was passedthrough a bubbler that had been filled with the solvent of choice.Saturation of the carrier gas with the solvent vapor was verifiedthrough measurement of the rate of mass loss of the solvent in thebubbler. The vapor-saturated carrier gas was then diluted with purecarrier gas through the use of mass flow controllers (MKS Instruments,Inc). The carrier gas for all experiments was oil-free air, obtainedfrom the general compressed air laboratory source, containing 1.10+/−0.15 parts-per-thousand (ppth) of water vapor. The air was filteredto remove particulates but deliberately was not dehumidified orotherwise purified to reproduce a range of potential “real world”operating environments. Calibration of the flow system using a flameionization detector (model 300 HFID, California Analytical Instruments,Inc.) Indicated that the delivered analyte concentrations were present.

[0058] Eight bubblers for generation of vapors were available, so the 22alcohols and 2 diols were divided into 3 groups of 8 as indicated inFIG. 5. To pre-condition the sensors, prior to each of the 3 runs, thesensors were subjected to 40 exposures, 5 to each of the 8 analytes.Data collection then consisted of a set of 10 exposures to the 8analytes, with 80 exposures performed in randomized order to eliminatesystematic errors from history effects. In the third run, bubbler 2 wasreplaced by a pyrex tube 37 cm in length with a 1 cm inner diameter.This tube was loaded with approximately 25 cm of granular, solidneopentanol. Flow rates were calculated to give 100 ml/min of saturatedvapor from the bubblers, which were of sufficient path length to providesaturated vapors. The background air flow was 1900 ml/min, so that theanalyte concentration delivered to the sensors was 5% of the analyte'ssaturated vapor pressure at room temperature. The ability of the vapordelivery system to provide the expected analyte concentrations based onthe input and control settings to the mass flow controllers as verifiedusing a calibrated flame ionization detector that sampled several testanalyte gas streams being delivered to the sensor chamber.

[0059] An exposure had 300 seconds of background air flow, followed by300 seconds of flow of analyte at 5% of its saturated vapor pressure,followed by 300 seconds of the background air. The DC resistance of eachsensor was measured at intervals of approximately 6 seconds using amultiplexing ohmmeter. The baseline resistance of a sensor was taken asan average of all measurements of the resistance of that sensor acquiredover a 60 second period that started between 60 and 66 seconds prior tothe start of the exposure to an analyte. The exact initiation time ofthis baseline resistance measurement was different for each sensor, dueto small variations in the time interval required to read the set of 20resistance values through the multiplexing ohmmeter. The resistanceresponse for each sensor to an analyte was taken as an average of allmeasurements for that sensor in a 60 second period that started between234 and 240 seconds after the beginning of the presentation of the vaporto the sensors, with the exact initiation time for each sensor channelstaggered similarity to that of the baseline resistance readings. Aresponse was taken to be the change in resistance of a sensor, ΔR,divided by its baseline resistance, ΔR. All differential resistancevalues (ΔR/R) used in the data analysis represented, or very closelyapproximated, the steady-state resistance readings obtained from thesensors during exposure to the analyte of interest.

[0060] Data Analysis. Initial raw data manipulation and calculation ofresponses was performed using Microsoft Excel. Multiple Linearregression (MLR) was performed using either Excel or the QSAR {Define}module of the Cerius2 program (Molecular Simulations, Inc.) on a SiliconGraphics O2 computer. Many possible MLR models were created, compared,cross-bred, and evolved by the genetic function approximation onCerius2.

[0061] Results. FIG. 2 presents the relative differential resistanceresponses for various conducting polymer composite sensors to threerepresentative alcohols, and FIG. 5 summarizes all of the sensorresponse data for the various alcohols investigated in this work. Eachalcohol produced a distinct, characteristic response pattern with thearray of sensors chosen for use in the work. Other sensor arrayscomprising different polymer formulatives are clearly capable ofproviding response patterns useful in the present invention.

[0062] The responses of the 19 working sensors to 20 of the alcohols(FIG. 5) were used to build a QSAR model. Benzyl alcohol and tert-amylalcohol were excluded from the fit because their biological activitieswere anomalous. The two diols were also excluded while building themodel.

[0063] The inhibitory action data of Cohen and Mannering (Mol.Pharmacol. 1973, 9, 383-397) are listed in FIG. 5. The values areexpressed as pI₅₀, where I₅₀ is the concentration of the alcohol (in mM)at which the activity of the enzyme is 50% inhibited, and pI₅₀ is thenegative logarithm of I₅₀. More positive numbers correspond to morestrongly inhibiting alcohols.

[0064] The QSAR equations consist of a linear combination of descriptorswhose coefficients are obtained by a least-squares fitting of predictedto observed biological activity through multiple linear regression.Equation 1 represents a general set of QSAR equations, $\begin{matrix}{{{A \cdot X_{1,1}} + {B \cdot X_{1,2}} + {C \cdot X_{1,3}} + \ldots \quad + {J \cdot X_{1,n}} + K} = Y_{1}} & \text{(1a)} \\{{{A \cdot X_{2,1}} + {B \cdot X_{2,2}} + {C \cdot X_{2,3}} + \ldots \quad + {J \cdot X_{2,n}} + K} = Y_{2}} & \text{(1b)} \\{\quad \vdots} & \quad \\{{{A \cdot X_{m,1}} + {B \cdot X_{m,2}} + {C \cdot X_{m,3}} + \ldots \quad + {J \cdot X_{m,n}} + K} = Y_{m}} & \text{(1m)}\end{matrix}$

[0065] where Y₁ is the biological activity of the i^(th) Molecule,X_(i,j) is the value of the j^(th) descriptor for the ith molecule, andA, B, C, . . . K are constants that are obtained through the fitting ofY₁, (predicted) versus Y₁ (observed). In Equation 1, the i^(th)alcohol's inhibitory activity is represented by Y₁ and its n sensorresponses are taken as its descriptors (X_(i,l) to X_(i,n))

[0066] The genetic function algorithm of the QSAR module of Cerius2 wasused to select the best sensors for the QSAR. One hundred multiplelinear regression models were generated from random combinations of 4sensors. These models were ranked according to a lack-of-fit (LOF)parameter, as given by equation 2: $\begin{matrix}{{LOF} = \frac{LSE}{\left( {1 - \left( {\left( {c + {dp}} \right)/m} \right)} \right)\bigwedge 2}} & (2)\end{matrix}$

[0067] LSE is the least-squares error, c and p are both the number ofdescriptors (sets of relative differential resistance response of thesensors in the array) for a simple linear model such as the one herein,M is the number of samples (e.g., alcohols), and d is the “smoothingparameter”, which is entered by the user (1.0 was used). The LOF valueis therefore an inverse measure of how well the model fits the data,with a penalty for the use of a large number of descriptors relative tosamples. From the set of 100 models, two “parents” are chosen, with aprobability inversely proportional to their LOF, and “crossed over”—someof the descriptors from each are used to form a new model. There is thena probability for “mutation”, where a new, randomly chosen, descriptoris added to the “daughter”. If the daughter is not already present inthe population, it replaces the model with the worst LOF from thepopulation. After 5,000 rounds of genetic operation, convergence isgenerally reached, in which the optimal models have been found.

[0068] When the 19 sets of responses from the working sensors were givento the Genetic Function Algorithm (GFA), a model that incorporated 5 ofthe sensors was found to be optimal. The best fit is described byequation 3:pI₅₀ = 0.51 − 3 + 1.90 − 9 − 3.58 − 13 − 2.14 − 15 − 0.90 − 18 − 1.29n = 20  R = 0.995  s = 0.092  F = 297

[0069] The numbers in bold refer to sets of responses from the sensorswith those numbers, n is the number of samples, R is the correlationcoefficient, and s is the standard error. The correlation coefficient of0.995 indicates that the fit was quite good. The F statistic of 297indicates that the overall significance of the fit is very high, in factis at a level of 1-10⁻¹³. Coefficients for all sensors are significantfar beyond the 99.9% level, as attested to by their t statistics (seetable 3). Predicted versus experimental pI₅₀ values are plotted in FIG.3. TABLE 3 Regression Statistics For the Coefficients of Equation 3Coefficient Standard Error t Stat P-value Intercept −1.29 0.27 −4.713.32E-04  3 0.51 0.07 6.93 6.98E-06  9 1.90 0.19 9.92 1.03E-07 13 −3.580.21 −17.13 8.70E-11 15 −2.14 0.27 −7.91 1.56E-06 18 −0.90 0.08 −11.341.94E-08

[0070] Methanol has an inhibition activity distinctly different fromthat of the other alcohols, and this can lead to a misleadingly good fitthrough a “point and cluster” effect. A second least-squares fitting ofequation 3 was performed with the exclusion of methanol. The coefficientof 15 changed from −2.14 to −2.20, while those of the other sensorsremained nearly the same. The overall quality of the fit declined; Fdecreased from 297 to 109, corresponding to a decrease in thesignificance of the fit from the level of 1-(1×10⁻¹³) to 1-(4×10⁻¹⁰).The decrease quality of the fit occurs because methanol is modeled wellby the equation, but when methanol is excluded there is much lessvariation in the data to be fit.

[0071] Electronic Nose-Based QSAR. The selection of which molecules toinclude in a QSAR is crucial. In the sense, that it is desirable to usethe broadest set of molecules available to build a QSAR, while notincluding only one or two molecules from a distinctly different class ofcompounds. For example, benzyl alcohol, the only aromatic alcohol in thedata set, has a higher activity than is predicted by both our QSAR andanother QSAR on the cytochrome P-450 system. The anomalous activity ofbenzyl alcohol could be accounted for with an additional descriptorunique to benzyl alcohol, but the choice of such a parameter is ratherarbitrary, so benzyl alcohol was excluded during the building of ourQSAR. Tert-amyl alcohol was also excluded because there is evidence thattertiary alcohols function through a stimulatory mechanism in additionto the usual inhibitory mechanism. As would be expected in tert-amylalcohol were also acting through this stimulatory mechanism, itsinhibitory activity is anomalously low. The two diols were also excludedwhile building the model. Because of these limitations, the QSAR isexpected to be most successful at predicting the activity of aliphaticmono-alcohols having no other functionalities.

[0072] The sensors chosen for the model by the GFA are among those whoseresponses are most reproducible. Reproducibility was measured byexamining the set of 10 response of a given sensor to a given analyte.The value S_(i,j) is defined as the standard deviation among the 10responses of the j^(th) sensor to the i^(th) alcohol divided by theaverage of those responses. Each sensor has a set of 20 S values, onefor each alcohol. A sensor's reproducibility can be gauged by the medianof its set of S values. Four of the five sensors used in the modeldisplayed median S values less than 0.063, raking them among the bestseven sensors. The only sensor outside this group, 15, responded only tovery polar analytes. Since its response to the majority of the analyteswas quite small, its S value for those analytes is very large. However,for the analytes to which it did respond, for example methanol andethanol, its S values are small, 0.040 and 0.041, respectively. Theinclusion of 15 might be questioned if it were necessary only to modelthe activity of one analyte, namely the outlier methanol. To test thevalidity of including 15 in the QSAR, equation 3 was refit with the sameset of sensors and all of the previously used alcohols, excludingmethanol. In the new QSAR, the significance of 15 remains significant.If the set of five sensor responses to methanol are substituted into thesecond QSAR equation, which was formed with no information aboutmethanol, the predicted pI₅₀ of methanol is −3.12 very close to itsexperimental value of −3.09. It appears that whatever molecularcharacteristics are probed by 15 are successfully extrapolated from themore moderately polar analytes to methanol. In other words, 15 is notjust an indicator variable for methanol that is fit with an arbitrarycoefficient.

[0073] A quantitative measure of the predictive power of the QSAR can beobtained by building a model using the biological and sensor responsedata from all the molecules except one, and then predicting the activityof the excluded molecule with that model. The procedure is repeated foreach molecule in the data set, and the predictive sum of squares (PRESS)is defined as the sum, over all analytes, of the squared differencesbetween the predicted and actual biological activity. Using equation 3,the PRESS for the set of 20 alcohols is 0.221. This value can becompared to the residual sum of squares, RSS, in which one QSAR equation(fit to all samples) is used to calculate the predicted activity. Aswould be expected, the RSS of 0.117 is lower than the PRESS. Moresignificantly, a large difference between the PRESS and RSS would implythat the model had used too many parameters and overfit the data, andthis appears not to be the case.

[0074] An optimum fit (as judged by the LOF parameter) was found torequire five descriptors; no equation with a different number ofdescriptors formed as significant a model. The best 4 sensors QSAR,consisting of sensors 1, 13, 16 and 17, has an R=0.984, s=0.163, andF=114, indicating an overall significance at the level of 1-(5×10⁻¹¹).On the other hand, addition of further sensors adds parameters andenables a better fit to the data set. However, if 4 is added to equation3 to form the best 6-sensor equation, certain key statistics point to adiminished model. As would be expected with an additional parameter, Rincreases, from 0.995 to 0.996. Additionally, the standard errordecreases from 0.0916 to 0.0834, the RSS decreases from 0.117 to 0.090,and the F statistic increases from 297 to 300. However, the significanceof the fit, represented by the F statistic, decreases from1-(1.08×10⁻¹³) to 1-(3.66×10⁻¹³). The PRESS increases from 0.221 to0.253. Thus, although the 6-sensor model fits the set of 20 alcoholsbetter than the 5-sensor model, the 6-sensor model is worse atpredicting the activity of an alcohol that was not included in the fit,indicating that the 6-sensor model has overfit the data.

[0075] As described above, the cytochrome P-450 p-hydroxylationinhibition activities of all the aliphatic mono-alcohols investigated inthis work could be quite accurately predicted from a model that wasconstructed without the use of any information about the molecularstructure of the alcohols for which the prediction are made. Thisindicates that the resistance data output of the electronic nosecontains implicit information on most of the chemical factors thatcontrol the interactions of the enzyme with the alcohols. Theseresistance data reflect the binding interactions between the alcoholsand a collection of polymers having a diverse collection of chemicalattributes. It is not necessary that an individual polymer probespecifically and exclusively one such descriptor of theanalyte-substrate interaction, because the desired information can beobtained through analysis of the collective response of the sensor arrayto an analyte.

[0076] Comparison with Other QSARs. Cohen and Mannering fit the activityof 11 of the unbranched 1 - and 2-alcohols (excluding methanol) to a oneparameter equation using log P (J. Mol. Pharmaco. 1973, 9, 383-397). Amodified version, using updated log P values and fit to only 10 alcohols(excluding methanol and ethanol), was given later by Shusterman(equation 4) (Chem.-Biol. Interactions 1990, 74, 63-77). $\begin{matrix}{{{pI50} = {{0.43\log \quad P} - 0.53}}{N = {{10\quad R} = {{0.954\quad s} = 0.128}}}} & (4)\end{matrix}$

[0077] However, Shusterman also showed that for a larger set ofalcohols, a simple fit to log P was inadequate to describe most of theiractivity; a fit of 19 alcohols yielded equation 5, which has rather poorregression statistics. $\begin{matrix}{{{pI50} = {{0.35\quad \log \quad P} - 0.71}}{n = {{19\quad R} = {{0.505\quad s} = 0.468}}}} & (5)\end{matrix}$

[0078] In a second equation using two descriptors, log P and (log p)^ 2,Cohen and Mannering fit 17 of the alcohols with an R of 0.98 (equation6). pI₅₀ = 1.50  log   P − 0.36(log   P)² + 1.75n = 17  R = 0.98  s = 0.44

[0079] Although this was a better fit, it used more descriptors.Additionally, it is evident from inspection of the data that there arefactors besides hydrophobicity that determine an alcohol's activity.Four subsequent QSARs have therefore been used to model; the data setmore fully and some aspects of these models are discussed below.

[0080] A more complex, three parameter, QSAR was based upon logP, acalculated electronic parameter (ε_(HOMO)), and a steric parameter(BULK_(lat))(equation 7). $\begin{matrix}{{{pI}_{50} = {{16.2\quad \log \quad P} - {16.0\quad {\log \left( {{\beta \quad P} + 1} \right)}} - {1.35\quad {BULK}_{lat}} + {0.381\quad ɛ_{HOMO}} + 22.5}}\quad {n = {{21\quad R} = {{0.982\quad s} = {{0.170\quad \log \quad \beta} = 1.05}}}}} & (7)\end{matrix}$

[0081] Shusterman and Johnson, however, pointed out that the use ofε_(HOMO) as a parameter was unjustified since it was necessary only tofit benzyl alcohol, and becomes an insignificant parameter (as indicatedby its t value) when benzyl alcohol is excluded from the data set.Similarly, the bilinear dependence of pI50 upon log P of equation 7 wasnecessary only to fit a single data point, methanol.

[0082] Another QSAR, based on a choice of molecular connectivityindices, has also been used to model the activity of 20 alcohols (benzylalcohol and tert-amyl alcohol were excluded (equation 8).$\begin{matrix}{{{pI50} = {{{- 6.88}\left( {1/^{o}\chi^{v}} \right)} - {1.14^{4}\chi_{PC}} + 1.85}}{n = {{20\quad R} = {{0.983\quad s} = 0.156}}}} & (8)\end{matrix}$

[0083] The parameter ^(o)χ^(v), the zero-order valence molecularconnectivity index, basically corresponds to molecular size, andtherefore hydrophobicity, for this set of molecules. Hence, the inverseof the index has a negative coefficient in equation 8. The parameter⁴χ_(PC), the fourth-order path/cluster molecular connectivity index,correlates with the degree of branching in the molecule, and thereforealso has a negative coefficient in equation 8.

[0084] A third QSAR, which relies entirely upon calculated electronicparameters as descriptors, has been constructed and used to fit all 22alcohols. Shusterman noted problems with the QSAR. For example, it wasasserted that the α-carbon of the alcohols was acting as an electronacceptor from the enzyme, because a correlation between activity andQCL, the electron density on the α-carbon in the LUMO, was found. QCL iscorrelated with log P(R=0.747), to some extent explaining the fit. Twoalcohols, 3-methylbutanol and 2,4-dimethyl-3-pentanol, were poorly fit,and no rationalization was presented for why the correlation with QCLwould not apply to these two substrates as well.

[0085] Finally, Shusterman created a QSAR based on log P and two stericparameters, M and A, which were used to describe the branching of thealcohols. M is the number of carbons beyond the methyl substituent inFIG. 3, thus, 1- and 2-alcohols have an M=0, while M for 3-pentanolwould be one, and for 2,4-dimethyl-3-pentanol is 2. The secondparameter, A, refers to the number of branched carbons in the mainchain, A=1 for 2-methyl-1-butanol and 2 for neopenyl alcohol. A fit of19 of the alcohols (benzyl alcohol, tert-amyl alcohol, and methanol wereexcluded) yielded equation 9. The negative coefficient for M and Aindicate the loss of activity with branching. $\begin{matrix}{{{pI50} = {{0.48\quad \log \quad P} - {0.65\quad \bullet \quad M} - {0.31\quad \bullet \quad A} - 0.60}}{n = {{19\quad R} = {{0.955\quad s} = 0.171}}}} & (9)\end{matrix}$

[0086] To compare the electronic nose QSAR to those of Sabljic andShusterman, one must use statistics that take into account the number ofdescriptors used. Table 4 lists the comparison of selected regressionstatistics from the QSAR of Sabljic, Shusterman, equation 3, and theQSAR created when the coefficients of equation 3 were fit to the 19alcohols besides methanol (R is the correlation coefficient, s is thestandard error, and the final column is the overall significance of theregression equation). Because the electronic nose QSAR model uses moreparameters, it is inappropriate to compare just either the correlationcoefficients, standard error, or residual sum of squares of the models.To some extent, the PRESS should be independent of the number ofparameters in a model, since the model is tested upon molecules aboutwhich it has no information. The PRESS of the electronic nose QSAR modelis significantly lower than the other two models of interest. Finally,the F steatitic gauges the overall significance of the fit whileaccounting for the number of parameters used. By this measure, theelectronic nose QSAR is approximately as significant as Sabljic's andmore significant than Shusterman's. TABLE 4 Data descriptors pts fitused R s RSS PRESS F Significance F Sabljic 20 2 0.983 0.156 0.414 0.872250 2.51E-13 Shusterman 19 3 0.956 0.17  0.436 0.786 53 3.34E-08 Present20 5 0.995 0.092 0.117 0.221 297 1.08E-13 Disclosure Present 19 5 0.9880.095 0.117 0.243 109 3.89E-10 Disclosure (no methanol)

[0087] It appears that the important chemical interaction involved inthe partitioning of the aliphatic alcohols into the enzyme binding siteare probed by the array responses. The construction of our QSAR did notrequire making assumption regarding which steric or electronic factorsare important or what parameters to use to capture such effects.Obtaining chemical insight into the nature of the dominant bindingforces involved in the reaction being modeled would require a completeunderstanding of the chemical factors that determine the analytepartitioning into each polymer in the electronic nose. In principle itis possible to extract such information for certain descriptors ofinterest, but it is not necessary to have such information in order touse the readily-obtained electronic nose data to predict successfullythe activity of various alcohols in inhibiting cytochrome P-450activity.

[0088] Although only a few embodiments have been described in detailabove, those having ordinary skill in the art will certainly understandthat many modifications are possible in the preferred embodiment withoutdeparting from the teachings thereof.

[0089] All such modifications are intended to be encompassed within thefollowing claims.

We claim:
 1. An analyte screening system, comprising: a sensor arraycomprising a plurality of different differentially responsive sensors,having a first signal profile produced by the plurality of differentdifferentially responsive sensors, when contacted with a first analyteand a second different signal profile produced when contacted with asecond analyte, wherein the difference between the first signal and thesecond signal being indicative of a difference in the property orproperties of the first analyte and second analyte; a measuring device,connected to the sensor array; and a computer; the measuring devicedetecting a signal in each of the plurality of different differentiallyresponsive sensors and the computer assembling the signal into a sensorarray signal profile; wherein the computer is operative to compare thesensor array signal profile to at least one previously obtained signalprofile indicating a standard sample having a specific activity,chemical or physical property, or function, wherein the comparison ofthe sensor_array signal profile to the at least one previously obtainedsignal profile is indicative of a specific activity, chemical orphysical property, or function of the analyte.
 2. The system of claim 1,wherein the analyte comprises a chemical.
 3. The system of claim 2,wherein the analyte comprises a chemical.
 4. The system of claim 3,wherein the biochemical is selected from the group consisting of alipid, hormone, fatty acids, nucleic acid, polypeptide, andcarbohydrate.
 5. The system of claim 4, wherein the polypeptide isselected from the group consisting of an antibody, enzyme, and protein.6. The system of claim 5, wherein the antibody is a monoclonal antibody,polyclonal antibody, humanized antibody, or fragments thereof.
 7. Thesystem of claim 5, wherein the enzyme is selected from the groupconsisting of lipases, esterases, proteases, glycosidases, glycosyltransferases, phosphateses, kinases, mono- and dioxygenases,haloperoxidases, lignin peroxidases, diarylpropane peroxidases, eposidehydrolases, nitrile hydrotases, nitrilases, transaminases, amidases, andacylases.
 8. The system of claim 1, wherein the specific activity isselected from the group consisting of enzymatic activity, bindingactivity, inhibitory activity, and modulating activity;
 9. The system ofclaim 1, wherein the signal profile of the standard sample is derivedfrom a library.
 10. The system of claim 9, wherein the library isgenerated by a neural network.
 11. The system of claim 1, wherein thedifferent differentially responsive sensors change optically,electrically, magnetically, mechanically, physically, or a combinationthereof.
 12. The system of claim 1, wherein the different differentiallyresponsive sensors are selected from the group consisting of crystallinecolloidal array (CCA) containing sensors, metal oxide sensors,dye-impregnated polymers coated onto beads of optically fibers, buldconducting organic polymers, capacitance sensors, chemically-sensitiveresistor sensors, and combinations thereof.
 13. The system of claim 12,wherein the chemically-sensitive resistor sensors are comprised ofregions of a non-conductive material and regions of a conductivematerial compositionally different than the non-conductive material,each resistor providing an electrical path through the regions ofconductive and non-conductive material, wherein interaction of themolecule with the resistor provides a change in resistance in theresistor.
 14. The system of claim 1, wherein the chemical or physicalproperty is selected from the group consisting of side groups, charge,hydrophobicity, polarity, molecular size or shape, and chirality. 15.The system of claim 1, wherein the different differentially responsivesensors are chemically sensitive resistors.